The processes by which new white matter lesions in multiple sclerosis (MS) develop are only partially understood. Much of this understanding has come through magnetic resonance imaging (MRI) of the human brain. One of the hallmarks of new lesion development in MS is enhancement on T1-weighted MRI scans following the intravenous administration of a gadolinium-based contrast agent that shortens the longitudinal relaxation time of the tissue. This visible enhancement in the MRI results from the opening of the blood-brain barrier and reveals areas of active inflammation. The incidence and number of existing enhancing lesions are common outcome measures used in MS treatment clinical trials. Dynamic-contrast-enhanced MRI (DCE-MRI) measures the rate at which contrast agents pass from the plasma to MS lesions. In this paper, we develop a model-free framework for the analysis of these data that provides biologically meaningful quantification of the blood-brain barrier opening in new MS lesions. To accomplish this, we use functional principal components analysis to study directions of variation in the voxel-level time series of intensities both within and across subjects. The analysis reveals and allows quantification of typical spatiotemporal enhancement patterns in acute MS lesions, providing measures of magnitude, rate, shape (ring-like vs. nodular), and dynamics (centrifugal vs. centripetal). Across 10 subjects with relapsing-remitting and primary progressive MS, we found subjects to have between 0 and 12 gadolinium-enhancing lesions, the majority of which enhanced centripetally. We quantified the spatiotemporal behavior within each of these lesion using novel measures. Further application of these techniques will determine the extent to which these lesion metrics can predict or track response to therapy or long-term prognosis in this disorder.
Shinohara, Russell T.; Crainiceanu, Ciprian; Caffo, Brian; Gaitán, María Inés; and Reich, Daniel, "POPULATION-WIDE MODEL-FREE QUANTIFICATION OF BLOOD-BRAIN-BARRIER DYNAMICS IN MULTIPLE SCLEROSIS" (January 2011). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 222.